Comparing Electric Driving Ranges of Battery Electric and Plug-in
Hybrid Electric Vehicles
Introduction
I am finding how does the electric driving range of battery electric
vehicles (BEVs) compare to plug-in hybrid electric vehicles (PHEVs).
BEVs can only run on electricity, however PHEVs have a small battery and
a gas engine, that gives hint about BEVs having a longer electric
range
I used my data-set from Electric Vehicle Population Data from the
Washington State Department of Licensing (data.wa.gov). It has 299705
observations and 16 variables where I only used “electric_vehicle_type”
that the vehicle type, and “electric_range” that shows miles the vehicle
can travel on electric power.
Additionally, there was several 0 values which was not recorded, so
I removed those to run the code smoothly.
Data Analysis
My first step, I loaded necessary libraries, read the data, and
explored it. I preferred to change the upper-cased variable to
lower-case, and replacing the space to “_”. I used different functions
like “select()”, to keep only required columns, and “filter()”, to
remove the 0 value or not recorded. Using “group_by()” and
“summarise()”, I calculated the mean value for each variable (BEV and
PHEV). Lastly, I made a bar graph which I find easy to compare the two
groups.
Loading libraries and data-set
library(tidyverse)
## Warning: package 'tidyr' was built under R version 4.6.1
## Warning: package 'readr' was built under R version 4.6.1
## Warning: package 'dplyr' was built under R version 4.6.1
## Warning: package 'lubridate' was built under R version 4.6.1
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.2.1 ✔ readr 2.2.0
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.3 ✔ tibble 3.3.1
## ✔ lubridate 1.9.5 ✔ tidyr 1.3.2
## ✔ purrr 1.2.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(ggplot2)
library(dplyr)
library(readr)
mydataset <- read_csv("export.csv")
## Rows: 299705 Columns: 16
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (10): VIN (1-10), County, City, State, Make, Model, Electric Vehicle Typ...
## dbl (6): Postal Code, Model Year, Electric Range, Legislative District, DOL...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
summary(mydataset)
## VIN (1-10) County City State
## Length :299705 Length :299705 Length :299705 Length :299705
## N.unique : 18376 N.unique : 255 N.unique : 941 N.unique : 50
## N.blank : 0 N.blank : 0 N.blank : 0 N.blank : 0
## Min.nchar: 10 Min.nchar: 3 Min.nchar: 3 Min.nchar: 2
## Max.nchar: 10 Max.nchar: 20 Max.nchar: 24 Max.nchar: 2
## NAs : 11 NAs : 11
##
## Postal Code Model Year Make Model
## Min. : 1030 Min. :1999 Length :299705 Length :299705
## 1st Qu.:98052 1st Qu.:2021 N.unique : 51 N.unique : 202
## Median :98134 Median :2023 N.blank : 0 N.blank : 0
## Mean :98176 Mean :2022 Min.nchar: 3 Min.nchar: 2
## 3rd Qu.:98391 3rd Qu.:2024 Max.nchar: 22 Max.nchar: 24
## Max. :99801 Max. :2027
## NAs :11
## Electric Vehicle Type Clean Alternative Fuel Vehicle (CAFV) Eligibility
## Length :299705 Length :299705
## N.unique : 2 N.unique : 3
## N.blank : 0 N.blank : 0
## Min.nchar: 30 Min.nchar: 37
## Max.nchar: 38 Max.nchar: 60
##
##
## Electric Range Legislative District DOL Vehicle ID Vehicle Location
## Min. : 0.00 Min. : 1.00 Min. : 4469 Length :299705
## 1st Qu.: 0.00 1st Qu.:17.00 1st Qu.:228301678 N.unique : 1199
## Median : 0.00 Median :32.00 Median :267927096 N.blank : 0
## Mean : 36.66 Mean :28.75 Mean :250992430 Min.nchar: 23
## 3rd Qu.: 32.00 3rd Qu.:42.00 3rd Qu.:284535905 Max.nchar: 27
## Max. :337.00 Max. :49.00 Max. :479114996 NAs : 20
## NAs :26 NAs :790
## Electric Utility 2020 GEOID
## Length :299705 Min. :1.001e+09
## N.unique : 77 1st Qu.:5.303e+10
## N.blank : 0 Median :5.303e+10
## Min.nchar: 10 Mean :5.297e+10
## Max.nchar: 112 3rd Qu.:5.305e+10
## NAs : 11 Max. :6.601e+10
## NAs :11
Changing uppercase to lowercase and space to “_”
names(mydataset) <- tolower(names(mydataset))
names(mydataset) <- gsub(" ","_", names(mydataset))
Finding average data
range <- mydataset |>
select(electric_vehicle_type, electric_range) |>
filter(electric_range > 0) |>
group_by(electric_vehicle_type) |>
summarise(number_of_vehicle = n(),
average_range = mean(electric_range, na.rm = TRUE)
)
range
## # A tibble: 2 × 3
## electric_vehicle_type number_of_vehicle average_range
## <chr> <int> <dbl>
## 1 Battery Electric Vehicle (BEV) 45492 201.
## 2 Plug-in Hybrid Electric Vehicle (PHEV) 57952 32.2
ggplot(range, aes(x = electric_vehicle_type, y = average_range, fill = electric_vehicle_type)) +
geom_col(show.legend = TRUE) +
labs(title = "Electric Range by Vehicle Type",
x = "Type of Vehicle",
y = "Electric Ranges (miles)") +
# Tilting the text to make it clear
theme(axis.text.x = element_text(angle = 10, hjust = .5))
Conclusion
This analysis gives the result that Battery Electric Vehicle have a
longer electric driving range than Plug-in Hybrid Electric Vehicles.
BEVs has averaged about 200.5 miles on electric power, where PHEVs
averaged only about 32.3 miles. BEVs is better for long distance on
electricity.
Cons is that most of the BEVs had a range of 0 and were removed. In
future research, I could find the model and its year, to find the
missing information.
References
Website: data.gov